Research collaboration in health management research communities

  • Chichen Zhang1,

    Affiliated with

    • Qi Yu2,

      Affiliated with

      • Qinghua Fan3 and

        Affiliated with

        • Zhiguang Duan4Email author

          Affiliated with

          BMC Medical Informatics and Decision Making201313:52

          DOI: 10.1186/1472-6947-13-52

          Received: 27 October 2012

          Accepted: 19 April 2013

          Published: 23 April 2013

          Abstract

          Background

          This study uses scientometrics methodology to reveal the status quo and emerging issues of collaboration in health management.

          Methods

          We searched all the articles with the keyword “health management” in the period 1999–2011 in Web of Knowledge, then 3067 articles were found. Methods such as Social network analysis (SNA), co-authorship, co-word analysis were used in this study.

          Results

          Analysis of the past 13 years of research in the field of health management indicates that, whether the production of scientific research, or authors, institutions and scientific research collaboration at the national level, collaboration behavior has been growing steadily across all collaboration types. However, the international scientific research cooperation about health management study between countries needs to be further encouraged. 17 researchers can be seen as the academic leaders in this field. 37 research institutions play a vital role in the information dissemination and resources control in health management. The component analysis found that 22 research groups can be regarded as the backbone in this field. The 8 institution groups consisting of 33 institutions form the core of this field. USA, UK and Australia lie in the center by cohesive subgroup analysis; Based on keywords analysis, 44 keywords with high frequency such as care, disease, system and model were involved in the health management field.

          Conclusions

          This study demonstrates that although it is growing steadily, collaboration behavior about health management study needs to be enhanced, especially between different institutions or countries/regions, which would promote the progress and internationalization of health management. Besides, researchers should pay attention to the cooperation of representative scholars and institutions, as well as the hot areas of research, because their experience would help us promote the research development of our nation.

          Keywords

          Health management Co-authorship Network Collaboration

          Background

          The idea and practice of health management originated from America in 1950s, and then sprung up as an emerging subject in UK, Germany, France, Japan etc. In the 21st century, health management spread in the developing countries and was applied in government, business, medical institutions and the insurance industry. Now it has become a prospective health service model for many countries to improve their national health level and promote the society’s sustainable development. Along with the increasingly popularity of health management, research collaboration in this field has also increased. Research collaboration is becoming an important way of improving health management by extensive cooperation, which makes resources sharing and knowledge stocking possible. However, the co-authorship analysis in this field is seldom reported. Thus, the status quo of international collaboration in health management was revealed by scientometric methodology in this study.

          Literature review

          It has been argued that co-authorship do not provide the entire view of the process of collaboration, however it is still advantageous for collaboration analysis through co-authorship as it is inexpensive and practical [14]. Co-authorship can be analyzed at three levels (authors, institutions and countries/regions), such as the analysis of different countries/regions, institutions and authors for a certain time. It is a way to reveal the interrelationships of the domain, the intensity of these relations [5, 6]. Also, all sorts of methods are applied to this field, including the frequently used Bibliometric techniques and social network analysis, as well as some new methods. Moreover, Zaida Chinchilla-Rodríguez had used blockmodeling to study the internal structure of co-authorship networks in the micro-level in 2012 [7].

          Publications with more than one author have been on the rise, with many studies showing this trend [811]. However, these trends are not uniform, and must be contextualized by domain, country conditions and field of study [1217]. Research in this respect has shown that there is a rise in institutional collaboration [18], but with the full caveats that this varies by discipline [19]. Especially in the biomedical fields, it tends to have high degrees of collaboration between institutions domestically, but not internationally [20].

          The increase in international collaboration is not only a trend of the 21st century, but one that has been noted in scientometric studies for over a decade [2126]. However, very few studies examined the collaboration activities in Health Management research field across multiple collaboration types [2731]. This study intends to address this issue.

          Research questions

          We intended to reveal current status of the collaboration activities and research topics in the Health Management field by using the method of co-authorship and co-word analysis so as to provide scientific evidence on research collaboration and suggestions for policymakers to establish a more efficient system for guiding and funding the Health Management research in the future.

          Research Question 1:

          What is the research collaboration trend in Health Management research?

          Research Question 2:

          Who/which are the most collaborative authors, institutions and countries/regions in Health Management research?

          Research Question 3:

          What are the research topics in Health Management research?

          Data and methods

          Date collection

          The documents which contain the word “Health Management” in their title, abstract or keywords were collected from the scientific literature database “Web of Knowledge”. The scope was limited to the years 1999 through 2011. All documents regardless of type (e.g. article, meeting abstract, proceedings paper, review, editorial material, book review, letter, note, etc.) were processed. All documents from the Science Citation Index Expanded (SCI-Expanded), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), Conference Proceedings Citation Index-Science (CPCI-S), and Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH) were taken into account. The query yielded 3067 records, each of which has author names, affiliations, titles, sources, abstracts, total citations, key words and cited references.

          Data Refinement

          Articles coauthored by authors from more than one institution were classified as multi-institutional collaboration. A paper coauthored by authors from different countries/regions was considered a multi-national paper.

          The names of authors and institutions have been normalized manually. For example, Zhao Y from Shanghai Univ was labeled “Zhao Y 1”, while Zhao Y from Sch Management Beihang Univ was labeled “Zhao Y 2”. Different variations of institution’s name were assigned to one name.

          Keywords Plus (Web of Knowledge supplied keywords in capital letters) is used in this paper.

          Methods

          In our previous studies, we revealed the collaboration activities in the oncology research field and cardiology and cardiovasology research field by means of coauthorship analysis, social network analysis and keyword analysis. We believe that these studies can provide suggestions for policy-maker in medical research management.

          Bibliometrics

          Bibliometrics is a quantitative analysis method by processing the literatures’ characteristics and using mathematics and statistics methods to describe, evaluate and predict the status and future of science and technology.

          Social network analysis

          Social network is a network of individuals’ communication including nodes and ties, especially for gaining one’s specific ends. The node represents the individual or institution in the network, while the tie represents the content or way of communication [32, 33]. Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) [3437].

          Pajek, a visualization toolkit for large-scale networks, was applied to map the collaboration. The node size in the graph is proportional to number of productions by authors, institutions or countries/regions, and the thickness of the lines represents the number of co-published papers.

          Centrality

          Centrality is an important index to analyze the network. Whether the individual or institution lies in the center of social network will determine its influence on the network and its speed to gain information. Centrality measure includes degree centrality, closeness centrality, and betweenness centrality.

          Degree Centrality of is defined as the number of ties that a node has. Degree Centrality represents the simplest notion of Centrality since it is just the number of neighbors of a node in the network.

          The Closeness Centrality of a node is the number of others nodes divided by the sum of all geodesic distances between the node and all others, where larger distances yield lower Closeness Centrality scores. The closer a node is to all other nodes, the easier information may reach it, the higher its Centrality.

          Betweenness Centrality rests on the idea that a node is more central if he is more important as an intermediary in the network. The Betweenness Centrality of a node is the proportion of all geodesics between pairs of other nodes that include the node.

          The three centrality metrics can help us identify the “important” persons or organizations in the network.

          N-cliques and M-core

          N-cliques insists that every member or a sub-group have a direct tie with each and every other member. M-core is a cohesive subgroup which meets the requirements that all line value in the subgroup are no less than M.

          Keyword co-occurrence analysis

          Methods such as co-citation analysis, bibliographic coupling analysis and keyword analysis can be used to reveal the hot research topics. Co-citation analysis, bibliographic coupling analysis are two citation-based approaches. As many of our records have no citations, we chose keyword analysis in our study. If two keywords co-occur in many articles, it implies the close links between the topics to which they refer. Therefore, the analysis of the keyword co-occur frequency could reflect the relationship of the subjects. The keyword co-occurrence has been used in many studies to reveal research hot topics of some specific field or discipline [3842]. In this study, keyword co-occurrence was used to provide an immediate picture of research collaboration topics in health management field.

          Results

          The trends of scientific production in the area of health management

          From 1999 to 2011, the total amount of research papers in the field of health management has a significant growth (Figure 1). Figure 1 shows the total number of papers published annually. Overall, in 13 years the number of published articles increased by nearly six times from 62 in 1999 to 421 in 2011.
          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig1_HTML.jpg
          Figure 1

          Evolution of publications in health management research from 1999 to 2011.

          The analysis of cooperation trends

          The trend of co-author

          Between 1999 and 2011, the collaboration among the staff of health management has increased significantly. Figure 2 displays the percentage of writers coauthored papers, institutions coauthored papers and nationality coauthored papers. Figure 3 reveals the change of the average article number for an author, institution or country. The ratio of coauthored papers increased from 66% in 1999 to 89% in 2011. The ratio of institutions coauthored papers and national coauthored papers showed a similar trend of growth. However, the ratio of papers coauthored by writers is significantly higher than the other two resources. The quantity of the average article number for an author went up from 3.27 in 1999 to 4.29 in 2011.
          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig2_HTML.jpg
          Figure 2

          Percentage of multi-entity publications in health management research, 1999–2011.

          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig3_HTML.jpg
          Figure 3

          Average numbers of different entities per paper in health management research, 1999–2011.

          The trend of institutional co-authorship

          As mentioned above, in the field of health management research, the ratio of papers coauthored by institution grew from 34% in 1999 to 59% in 2011. Meanwhile, the average article number for institution climbed from 1.75 in 1999 to 2.24 in 2011.

          The trend of national co-authorship

          As shown in figure 2, the ratio of national coauthored papers in the field of health management research increased from 12% in 1999 to 18% in 2011. Figure 3 shows, most of the papers are the achievements of cooperation within a country, and the average article number for a country increased from 1.15 in 1999 to 1.26 in 2011, which increased slowly compared with that of researchers coauthored papers and institutions coauthored papers.

          Collaborations among researchers

          Co-author network

          The co-authorship network in this study contains 9447 nodes (researchers), 22666 lines (co-author frequency). The maximum co-author frequency is 8 and the network density is 0.000508.

          Centrality analysis

          There are 17 authors who ranked in top 100 of all the three centralities in the co-authorship network, measured by calculating the degree, closeness and betweenness centrality (See Table 1).
          Table 1

          Top 17 authors with high centralities

          Rank

          Authors

          Degree

          Closeness

          Betweeness

          1

          "O'Toole, T1"

          57

          0.006160686

          1.38332E-05

          2

          "Ascher, MS"

          51

          0.005938011

          7.10807E-06

          3

          "Tonat, K2"

          46

          0.005764384

          9.00965E-06

          4

          "Osterholm, MT2"

          46

          0.005764384

          5.15801E-06

          5

          "Perl, TM2"

          45

          0.005730871

          4.96446E-06

          6

          "Hauer, J2"

          40

          0.005568982

          3.76404E-06

          7

          "Layton, M3"

          40

          0.005568982

          3.76404E-06

          8

          "Lillibridge, S"

          40

          0.005187946

          3.13349E-06

          9

          "Arase, Y"

          39

          0.004234148

          8.41763E-06

          10

          "Friedlander, AM"

          37

          0.004831911

          2.43051E-05

          11

          "Swearingen, K"

          34

          0.004401286

          4.85667E-05

          12

          "Byington, CS1"

          33

          0.005189133

          9.19832E-05

          13

          "Eitzen, EM"

          29

          0.005243137

          3.0151E-05

          14

          "Pecht, M1"

          28

          0.004568423

          0.00012659

          15

          "Schmaljohn, AL"

          28

          0.004953315

          1.21053E-05

          16

          "Peters, CJ"

          28

          0.004879751

          6.25438E-06

          17

          "Roemer, MJ"

          24

          0.005683336

          6.23491E-05

          Cohesive subgroup analysis

          The N-clique of co-authorship network in health management was shown in Table 2. The maximum clique is 31-clique and 91.66% of researchers belong to 9-clique or below. Moreover, the majority researchers belong to 2-clique, 3-clique and 4-clique, the number is 1428, 1459, 1352 respectively.
          Table 2

          The N-clique of co-authorship network in health management

          N-Clique

          Freq

          Freq%

          CumFreq%

          N-Clique

          Freq

          Freq%

          CumFreq%

          0

          512

          5.42

          5.42

          11

          118

          1.25

          94.63

          1

          1009

          10.68

          16.10

          12

          117

          1.24

          95.87

          2

          1428

          15.12

          31.22

          13

          84

          0.89

          96.76

          3

          1459

          15.44

          46.66

          14

          74

          0.78

          97.54

          4

          1352

          14.31

          60.97

          15

          48

          0.51

          98.05

          5

          1141

          12.08

          73.05

          17

          47

          0.50

          98.55

          6

          806

          8.53

          81.58

          18

          33

          0.35

          98.90

          7

          469

          4.96

          86.55

          21

          22

          0.23

          99.13

          8

          252

          2.67

          89.21

          23

          24

          0.25

          99.39

          9

          231

          2.45

          91.66

          25

          26

          0.28

          99.66

          10

          163

          1.73

          93.38

          31

          32

          0.34

          100.00

          The M-core of co-authorship network in health management was shown in Table 3. The maximum core is 8-core. And the majorities are in 1-core, which contains 8181 researchers.
          Table 3

          The M-core of co-authorship network in health management

          M-Core

          Freq

          Freq%

          CumFreq

          CumFreq%

          0

          512

          5.42

          512

          5.42

          1

          8181

          86.60

          8693

          92.02

          2

          571

          6.04

          9264

          98.06

          3

          113

          1.20

          9377

          99.26

          4

          37

          0.39

          9414

          99.65

          5

          17

          0.18

          9431

          99.83

          6

          11

          0.12

          9442

          99.95

          7

          3

          0.03

          9445

          99.98

          8

          2

          0.02

          9447

          100.00

          By component analysis of 70 researchers who are higher than 4-core in the co-authorship, 22 groups were found (Figure 4), which means that the authors in each group co-published no less than 4 papers. And the relations among researchers within those groups are tight and stable.
          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig4_HTML.jpg
          Figure 4

          Collaboration among authors (line value>4).

          Collaborations among research institutions

          Multi-institutional collaboration network

          The institution collaboration network contains 2776 nodes (research institutions), 4461 lines. The maximum line value is 9 and the network density is 0.0011582.

          Centrality analysis

          There are 37 institutions that ranked in top 100 in the network, by calculating of degree, closeness and betweenness centrality (See Table 4).
          Table 4

          Top 37 institutions with high centralities

          Rank

          Org

          Degree

          Closeness

          Betweeness

          1

          UNIV MINNESOTA

          65

          0.139536034

          0.026331189

          2

          USAF

          61

          0.133243448

          0.020084938

          3

          UNIV WASHINGTON

          59

          0.136385856

          0.032965808

          4

          UNIV MARYLAND

          55

          0.123266575

          0.020476832

          5

          CTR DIS CONTROL & PREVENT

          42

          0.130342946

          0.011750547

          6

          UNIV MICHIGAN

          40

          0.122175917

          0.015957716

          7

          WHO

          39

          0.123311505

          0.016469697

          8

          STANFORD UNIV

          38

          0.13350636

          0.029782294

          9

          COLUMBIA UNIV

          38

          0.130670187

          0.017456569

          10

          JOHNS HOPKINS UNIV

          34

          0.128486561

          0.020521669

          11

          UNIV ALABAMA

          32

          0.125066586

          0.008339021

          12

          UNIV MELBOURNE

          32

          0.117405905

          0.019133512

          13

          US DEPT HHS

          32

          0.129817773

          0.008307802

          14

          UNIV MISSOURI

          30

          0.125484093

          0.005603591

          15

          UNIV SO CALIF

          30

          0.123423975

          0.004564441

          16

          MINIST HLTH

          29

          0.124217043

          0.034480061

          17

          NORTHWESTERN UNIV

          29

          0.128462166

          0.022695165

          18

          MED COLL GEORGIA

          29

          0.128291663

          0.003952521

          19

          TEL AVIV UNIV

          27

          0.118123294

          0.008396166

          20

          LONDON SCH HYG & TROP MED

          26

          0.127927818

          0.014331175

          21

          BETH ISRAEL DEACONESS MED CTR

          26

          0.126209705

          0.006820911

          22

          UNIV WISCONSIN

          24

          0.121386837

          0.007261674

          23

          UNIV WESTERN AUSTRALIA

          24

          0.115857916

          0.007170564

          24

          NIH

          24

          0.131151431

          0.007359746

          25

          UNIV ILLINOIS

          23

          0.118661913

          0.005502203

          26

          HARVARD UNIV

          22

          0.120264883

          0.014331563

          27

          ALBERT EINSTEIN COLL MED

          22

          0.122729953

          0.003980603

          28

          UNIV CALIF LOS ANGELES

          21

          0.125740612

          0.003765431

          29

          UNIV CALIF SAN FRANCISCO

          21

          0.126021648

          0.005312616

          30

          UNIV PENN

          20

          0.130117352

          0.018659974

          31

          BOSTON UNIV

          20

          0.125717248

          0.01613023

          32

          PENN STATE UNIV

          17

          0.11763043

          0.00749422

          33

          DUKE UNIV

          17

          0.118516418

          0.007325933

          34

          MCMASTER UNIV

          17

          0.11857873

          0.003277398

          35

          PALO ALTO MED FDN

          16

          0.122774493

          0.006299589

          36

          CHILDRENS HOSP

          15

          0.118745214

          0.007173336

          37

          UNIV COLORADO

          14

          0.115561098

          0.003574301

          Analysis of cohesive subgroups

          The N-clique of institution collaboration network in health management was shown in Table 5. The maximum is 16-clique. The M-core of health management institution collaboration network was shown in Table 6. The maximum is 9-core. And the majorities are in 1-core, which contains 1932 institutions. By component analysis of the 33 institutions that are in 3-core or more, 8 groups were found (Figure 5).
          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig5_HTML.jpg
          Figure 5

          Collaboration among institutions (line value>3).

          Table 5

          The N-clique of institution collaboration network in health management

          Cluster

          Freq

          Freq%

          Representative

          0

          651

          23.451

          SCI MONITORING INC

          1

          594

          21.3977

          UNIV ABERDEEN

          2

          544

          19.5965

          BOEING CO

          3

          330

          11.8876

          IMPACT TECHNOL LLC

          4

          190

          6.8444

          UNIV GUELPH

          5

          171

          6.1599

          MICHIGAN STATE UNIV

          6

          95

          3.4222

          NASA

          7

          28

          1.0086

          UNIV MICHIGAN

          8

          49

          1.7651

          WHO

          9

          16

          0.5764

          LONDON SCH HYG & TROP MED

          10

          19

          0.6844

          UNIV WISCONSIN

          11

          33

          1.1888

          UNIV MARYLAND

          13

          25

          0.9006

          UNIV WASHINGTON

          14

          14

          0.5043

          USAF

          16

          17

          0.6124

          UNIV ALABAMA

          Table 6

          The M-core of health management institution collaboration network

          Cluster

          Freq

          Freq%

          Representative

          0

          651

          23.451

          SCI MONITORING INC

          1

          1932

          69.5965

          PENN STATE UNIV

          2

          151

          5.4395

          NASA

          3

          23

          0.8285

          MINIST HLTH

          4

          2

          0.072

          UNIV SOUTHAMPTON

          5

          11

          0.3963

          UNIV SAO PAULO

          6

          1

          0.036

          JOHNS HOPKINS UNIV

          7

          3

          0.1081

          PREVENT

          9

          2

          0.072

          UNIV MARYLAND

          Collaboration among countries/regions

          Multi-national collaboration network

          The countries/regions cooperation network is structured by data analysis which contains 102 nodes (countries/regions), 358 lines. The maximum frequency of co-nation is 18 and the network density is 0.0683243.

          Analysis of cohesive subgroups

          The N-clique of countries/regions collaboration network in health management was shown in Table 7. The maximum is 9-clique. The M-core of multi-national collaboration network in health management was shown in Table 8. The maximum is 19-core. And the majorities are in 1-core, which contains 41 countries/regions. The collaboration network among the 30 countries/regions was shown in Figure 6. And we can find that USA, UK, and Australia are at the core of the map.
          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig6_HTML.jpg
          Figure 6

          Collaboration among Countries/regions (line value>3).

          Table 7

          The N-clique of countries/regions collaboration network in health management

          N-clique

          Freq

          Freq%

          Representative

          0

          17

          16.67

          SERBIA

          1

          10

          9.80

          CHILE

          2

          15

          14.71

          MEXICO

          3

          7

          6.86

          PAKISTAN

          5

          7

          6.86

          KOREA

          6

          10

          9.80

          BRAZIL

          7

          4

          3.92

          CHINA

          8

          10

          9.80

          CANADA

          9

          22

          21.57

          USA

          Table 8

          The M-core of multi-national collaboration network in health management

          m-core

          Freq

          Freq%

          Representative

          0

          18

          17.4757

          SERBIA

          1

          41

          39.8058

          FINLAND

          2

          14

          13.5922

          BRAZIL

          3

          7

          6.7961

          JAPAN

          4

          5

          4.8544

          TAIWAN

          5

          5

          4.8544

          SPAIN

          6

          3

          2.9126

          ITALY

          8

          2

          1.9417

          GERMANY

          9

          1

          0.9709

          SCOTLAND

          11

          3

          2.9126

          AUSTRALIA

          17

          1

          0.9709

          CHINA

          19

          3

          2.9126

          USA

          Analysis of hot research areas

          Co-occurrence network

          The keywords co-occurrence network contains 4356 nodes (keywords), 31628 lines (frequency of co-occurrence). The maximum frequency of co-occurrence is 97 and the network density is 0.0033345.

          Analysis of co-occur network

          As shown in Table 9, there are 44 keywords with frequency more than 20. Based on the m-core analysis, 44 keywords which is higher than 6-core were selected to form the network of Figure 7.
          http://static-content.springer.com/image/art%3A10.1186%2F1472-6947-13-52/MediaObjects/12911_2012_669_Fig7_HTML.jpg
          Figure 7

          Keywords with co-occurrence frequency > 6.

          Table 9

          44 keywords with frequency more than 20

          Rank

          Keyword

          Frequency

          1

          PUBLIC-HEALTH MANAGEMENT

          171

          2

          CARE

          120

          3

          MANAGEMENT

          85

          4

          DISEASE

          84

          5

          SYSTEM

          79

          6

          UNITED-STATES

          76

          7

          MODEL

          71

          8

          HEALTH

          68

          9

          RISK

          61

          10

          PREVALENCE

          59

          11

          IMPACT

          57

          12

          RANDOMIZED CONTROLLED-TRIAL

          55

          13

          POPULATION

          53

          14

          QUALITY

          48

          15

          INFECTIONS

          46

          16

          CHILDREN

          43

          17

          ANTHRAX

          42

          18

          MORTALITY

          40

          19

          PERFORMANCE

          40

          20

          SERVICES

          38

          21

          DIAGNOSIS

          37

          22

          INTERVENTION

          36

          23

          RISK-FACTORS

          35

          24

          OUTCOMES

          35

          25

          INHALATIONAL ANTHRAX

          34

          26

          COST

          33

          27

          ADULTS

          33

          28

          SMALLPOX

          32

          29

          PROGRAMS

          32

          30

          WOMEN

          30

          31

          PREVENTION

          29

          32

          TRIAL

          27

          33

          BEHAVIOR

          26

          34

          HEALTH MANAGEMENT

          25

          35

          HEALTH-CARE

          25

          36

          QUALITY-OF-LIFE

          24

          37

          TRANSMISSION

          23

          38

          PHYSICIANS

          23

          39

          RELIABILITY

          22

          40

          PRIMARY-CARE

          22

          41

          IDENTIFICATION

          22

          42

          EPIDEMIOLOGY

          21

          43

          DISEASE MANAGEMENT

          20

          44

          COMMUNITY

          20

          The 44 keywords form a series of concentric circles with the most frequent sequence. Public Health Management, Care and Disease are placed in the center (Figure 7). Keywords in the innermost circle are those which have closest collaboration with Public Health Management. Keywords in the second innermost circle have closest collaboration with those which are in the innermost circle, and so on.

          Discussion

          The analysis of cooperation trend

          Many studies have reported the ascending cooperation trend both in agencies and national cooperation. On the contrary, much less attention has been paid in the area of health management. As far as papers coauthored by author, there was a relatively high degree of cooperation in the field of health management research, between 1999 and 2011, 81% of output is the results of the research cooperation. Considering agencies and national cooperation level, however, the degree of cooperation is relatively lower, which has caused gaps by contrast with researcher cooperation level. Especially at the national level of cooperation, only 11% output is the results of international cooperation in the past 13 years, a little lower than 13% output in the Coronary Heart Disease research field in our previous study [43]. Therefore, the strengthening of international cooperation in health management research should be encouraged.

          The analysis of collaboration researchers

          The maximum frequency of co-authorship is 8, which indicate that the collaboration in health management field is not tight comparing with other fields, such as oncology or cardiovascular field [44, 45]. According to the centrality analysis, researchers such as O'Toole T1, Ascher Ms and Tonat K2 can be seen as the academic leaders in this field. According to the N-clique analysis and M-core analysis, the majority of researchers are in low N-clique and M-core, which once again proved that the research collaboration in health management research is not tight. The component analysis found that 22 research groups can be regarded as the backbone in this field. Therefore, the researchers in health management should strengthen their collaboration to improve the development and academic level of this field.

          The analysis of collaboration research institutions

          Judging from the centrality, 37 research institutions such as UNIV MINNESOTA, USAF, UNIV WASHINGTON and UNIV MARYLAND play an important role in the information dissemination and resources control in health management. Similar to previous study in oncology or cardiovascular field [4446], while in N-clique and M-core analysis, the frequency of 2- or 3-institutional collaboration is higher, which indicates that it is an irresistible trend that the scientific manpower of different institutions should be integrated. The 8 groups in Figure 5, formed by 33 institutions, co-published more than 3 times, could be regarded as the backbone in this field. It suggests that although to some extent there is collaboration among institutions in health management field, the level is not tight and stable. The government should encourage institutional collaboration to make their respective advantages complementary to each other, thereby, to further enhance the scientific research level. As depicted in Figure 5, extensive research collaboration existed in institutions of different types. For example, collaboration between university and hospital (CONCORD HOSP, UNIV WESTERN AUSTRALIA); collaboration between universities (UNIV FED RIO GRANDE DO SUL, UNIV SAO PAULO, UNIV FED SAO PAULO); collaboration among university, organization and government (WHO, CTR DIS CONTROL & PREVENT, JOHNS HOPKINS UNIV).

          The analysis of collaboration countries/regions

          According to the N-clique and M-core analysis, international collaboration in health management is becoming an irresistible trend. Previous research showed that economic factor will improve the research collaboration [47, 48]. And as shown in Figure 6, similar to previous study in oncology or cardiovascular field, those economic powers such as USA, UK are in the center of the network, which play an vital role in the information dissemination and resources control in health management. Although in developing countries/regions, such as China, research about health management started late, it has quickly becoming popular, and a broad collaboration network is forming. At the same time, other countries/regions which are less developed than China should also actively learn and cooperate with economic powers to enhance their scientific research level, change their position in information dissemination and control in this field, and to achieve the global balance development of health management.

          Analysis of hot research areas

          To some degree, the frequency of keywords could reflect the hot research areas of health management around the world from 1999 to 2011, providing useful experience for researchers and policy makers. Normally, the highest frequency keywords tend to be the basic words of the field which are unable to be the reference of topic analysis. As the frequency of Public-Health Management and Care are far much higher than other keywords, they are basic words in this study. Besides, the keywords Management, Disease and System having higher frequency reflect the status quo of health management research as well. For instance, Disease shows that management of related diseases (especially Chronic diseases) and preventive care of high-risk groups are the key of health management research while system and model show that the constructions of health management system and model is one of the hot research topics. Risk, Prevalence, and Impact also indicates that researchers focus on the fields of health risk assessment and health management effect evaluation. Therefore, research of health management is mainly reflected in clinical medicine and preventive medicine, which refer to the groups of public, women and children. Research methods are concentrated in statistical analysis such as randomized controlled trials, etc. As for these high-frequency keywords involve cutting-edged issues, covering wide range, the higher scientific research productivity or cooperation of several institutes are one of the effective way to solve the problem.

          Limitations

          This study using scientometrics methodology focuses mainly on the research collaboration among authors, institutions and countries/regions in Health Management research field with a view to some reference. With limited resources and research levels, this study only searched all the articles in the period 1999–2011 in Web of Knowledge, which content has certain limitations. Methods of SNA, co-authorship etc. are relatively fresh perspective but lacking innovation in this field. And lacking of regularity in the key word selection also impacts the analysis process. Besides, how is the research collaboration related with the research quality of the authors? What factors contribute to research collaboration? All of these need to be further investigated in future study.

          Conclusions

          Collaboration in health management field needs to be enhanced

          The number of publications in the health management field is showing a rising trend, especially in recent years. Co-authorship is also keeping growing. And the cooperation of authors is obviously higher than that by institutions and countries. 22 research groups and 37 institutions devoted in this field, among which researchers or research team of USA and UK are in the core position in the collaboration network. Reviewing the related articles in other fields and comparing them with the research results in oncology or cardiovascular field in earlier stage, though 81% of the articles are produced by scientific cooperation, the Cooperation intensity in the field of health management is still relatively weak, especially between institutions and countries. Therefore, the important way of promoting the progress and internationalization of health management is to strengthen the cooperation between countries and institutions and take full advantage of the core role of dominant groups.

          Representative countries and authors are found in the networks

          According to the centrality analysis, researchers such as O'Toole T1, Ascher Ms and Tonat K2 are representative to some extent in health management field. When it comes to institutions, network consisted of UNIV MARYLAND, USAF, CTR DIS CONTROL & PREVENT, WHO, MINIST HLTH is not only related to different types of institutions, but also shows the complicated relationship among them. Cooperating between countries/regions, USA and UK are in the center of the network which play leading roles in the information dissemination and resources control in health management. In order to provide a basis for the understanding of health management status quo and development trend, researchers should pay attention to the cooperation of representative scholars or institutions. They should also follow the cooperation model to study from the veteran organizations or institutions, take the opportunity of communication and cooperation, so as to promote the further research of their country or groups by using experience of countries or regions with developed health management research.

          The research topics in health management research

          According to keywords analysis, in spite of its wider range, health management research relatively focuses on the clinical medicine and preventive medicine, which involve the groups of public, women and children. Research methods are also concentrated in statistical analysis such as randomized controlled trials, etc. In health management field of 1999–2011, the hot research topics, helping providing useful experience for researchers and policy makers, included that health risk warning of public, constructions of health management model and system, disease management and effect evaluation of health management, which provide useful basis for research direction.

          In conclusion, by scientometrics methodology, this study analysing multiple collaboration types in Health Management research reveals the status quo, points out its defects and then comes up with related suggestions on strengthening cooperation of health management, analyzes the hot research topics as well. These results and proposal would provide an important reference for scholars, policy makers and managers on the aspect of researching and practicing health management deeply.

          Declarations

          Acknowledgements

          The research reported in this paper is done as part of the ‘Cooperation Analysis of Technology Innovation Team Member Based on Knowledge Network-Empirical Evidence in the Biology and Biomedicine Field’ (No. 71103114), which is supported by National Natural Science Foundation of China. And it is also supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (No. 20111009).

          Authors’ Affiliations

          (1)
          School of Public Health, Shanxi Medical University
          (2)
          Department of Information Management, Shanxi Medical University
          (3)
          Division of International Cooperation and Exchange, Shanxi Medical University
          (4)
          School of Public Health, Shanxi Medical University

          References

          1. Melin G, Persson O: Studying research collaboration using co-authorships. Scientometrics 1996, 36:363–77.View Article
          2. Vuckovic-Dekic L: Authoship-coauthorship. Arch Oncol 2003, 11:211–2.View Article
          3. Subramanyam K: Bibliometric studies of research collaboration: a review. J Inf Sci 1983, 6:33–8.View Article
          4. Katz JS, Martin BR: What is research collaboration? Res Policy 1997, 26:1–18.View Article
          5. Chinchilla-Rodríguez Z, Vargas-Quesada B, Hassan-Montero Y, González-Molina A, Moya-Anegóna F: New approach to the visualization of international scientific collaboration. Inf Vis 2010,9(4):277–287.View Article
          6. Moya-Anegón F, Chinchilla-Rodríguez Z, Vargas-Quesada B, CoreraÁlvarez E, González-Molina A, Muñoz-Fernández FJ, Herrero-Solana V: Coverage analysis of Scopus: A journal metric approach. Scientometrics 2007,73(1):57–58.View Article
          7. Chinchilla-Rodríguez Z, Ferligoj A, Miguel S, Kronegger L, Moya-Anegón F: Blockmodeling of co-authorship networks in library and information science in Argentina: a case study. Scientometrics 2012, 93:699–717.View Article
          8. Schmoch U, Schubert T: Are international co-publications an indicator for quality of scientific research? Scientometrics 2008,74(3):361–377.View Article
          9. Cronin B, Shaw D, La Barre K: A cast of thousands: Coauthorship and subauthorship collaboration in the 20th century as manifested in the scholarly journal literature of psychology and philosophy. J Am Soc Inf Sci Technol 2003,54(9):855–871.View Article
          10. Moody J: The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. Am Sociol Rev 2004,69(2):213–238.View Article
          11. Persson O, Glänzel W, Danell R: Inflationary bibliometric values: The role of scientific collaboration and the need for relative indicators in evaluative studies. Scientometrics 2004,60(3):421–432.View Article
          12. Larivière V, Gingras Y, Archambault E: Canadian collaboration networks: A comparative analysis of the natural sciences, social sciences and the humanities. Scientometrics 2006,68(3):519–533.View Article
          13. Abt HA: The frequencies of multinational papers in various sciences. Scientometrics 2007,72(1):105–115.View Article
          14. Glänzel W, De Lange C: A distributional approach to multinationality measures of international scientific collaboration. Scientometrics 2002, 54:75–89.View Article
          15. Melin G: Pragmatism and self-organization: Research collaboration on the individual level. Res Policy 2000, 29:31–40.View Article
          16. Egghe L: An explanation of the relation between the fraction of multinational publications and the fractional score of a country. Scientometrics 1999, 45:291–310.View Article
          17. Kliegl R, Bates D: International collaboration in psychology is on the rise. Scientometrics 2011, 87:149–58.View Article
          18. Qin J: An investigation of research collaboration in the sciences through the Philosophical Transactions 1901–1991. Scientometrics 1994,29(2):219–238.View Article
          19. Lundberg J, Tomson G, Lundkvist I, Skar J, Brommels M: Collaboration uncovered: Exploring the adequacy of measuring university-industry collaboration through co-authorship and funding. Scientometrics 2006,69(3):575–589.View Article
          20. Thijs B, Glänzel W: A structural analysis of collaboration between European research institutes. Research Evaluation 2010,19(1):55–65.View Article
          21. Zitt M, Bassecoulard E, Okubo Y: Shadows of the past in international cooperation: Collaboration profiles of the top five producers of science. Scientometrics 2000,47(3):627–657.View Article
          22. Schubert A, Braun T: International collaboration in the sciences, 1981–1985. Scientometrics 1990, 19:3–10.View Article
          23. Schubert A, Braun T: International collaboration in the sciences, 1981–1985. Scientometrics 1990, 19:3–10.View Article
          24. Hayati Z, Didegah F: International scientific collaboration among Iranian researchers during 1998–2007. Library Hi Tech 2010,28(3):433–466.View Article
          25. Wagner CS, Leydesdorff L: Mapping the network of global science: comparing international co-authorships from 1990 to 2000. Int J Technol Glob 2005,1(2):185–208.View Article
          26. Dore JC, Ojasoo T, Okubo Y, Durand T, Dudognon G, Miquel JF: Correspondence factor analysis of the publication patterns of 48 countries over the period 1981–1992. J Am Soc Inf Sci 1996,47(8):588–602.View Article
          27. Georghiou L: Global cooperation in research. Res Policy 1998,27(6):611–626.View Article
          28. Glänzel W: National characteristics in international scientific coauthorship relations. Scientometrics 2001,51(1):69–115.View Article
          29. Chinchilla-Rodríguez Z, Benavent-Pérez M, de Moya-Anegón F: International collaboration in medical research in Latin America and the Caribbean (2003–2007). J Am Soc Inf Sci Technol 2012,63(11):2223–2238.View Article
          30. Zheng J, Zhao Z-Y, Zhang X, Chen D-Z, Huang M-H, Lei X-P: International scientific and technological collaboration of China from 2004 to 2008: a perspective from paper and patent analysis. Scientometrics 2012, 91:65–80.View Article
          31. Toivanen H: Ponomariov B:African regional innovation systems: bibliometric analysis of research collaboration patterns 2005–2009. Scientometrics 2011, 88:471–93.View Article
          32. He B, Ding Y, Ni C: Mining enriched contextual information of scientific collaboration: A meso perspective. J Am Soc Inf Sci Technol 2011, 62:831–45.View Article
          33. Oliveira M, Gama J: An overview of social network analysis. Wiley Interdiscip Rev-Data Mining Knowl Discov 2012, 2:99–115.View Article
          34. Uddin S, Hossain L, Abbasi A, Rasmussen K: Trend and efficiency analysis of co-authorship network. Scientometrics 2012, 90:687–99.View Article
          35. Kronegger L, Mali F, Ferligoj A, Doreian P: Collaboration structures in Slovenian scientific communities. Scientometrics 2012, 90:631–47.View Article
          36. Lee B, Kwon O, Kim HJ: Identification of dependency patterns in research collaboration environments through cluster analysis. J Inf Sci 2011, 37:67–85.View Article
          37. Chinchilla-Rodrígueza Z, Vargas-Quesadab B, Hassan-Monterob Y: Antonio, González-Molinab, Félix Moya-Anegóna: New approach to the visualization of international scientific collaboration . Inf Vis 2010,9(4):277–287.View Article
          38. Zhang J, Wolfram D, Wang P, Hong Y, Gillis R: Visualization of health-subject analysis based on query term co-occurrences. J Am Soc Inf Sci Technol 2008, 59:1933–47.View Article
          39. Liu GY, Hu JM, Wang HL: A co-word analysis of digital library field in China. Scientometrics 2012, 91:203–217.View Article
          40. Zhao L, Zhang Q: Mapping knowledge domains of Chinese digital library research output, 1994–2010. Scientometrics 2011, 89:51–87.View Article
          41. Ding Y, Chowdhury GG, Foo S: Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management 2001, 37:817–42.View Article
          42. Sternitzke C, Bergmann I: Similarity measures for document mapping: A comparative study on the level of an individual scientist. Scientometrics 2009, 78:113–30.View Article
          43. Qi Y, Hongfang S, Zhiguang D: The research collaboration in Chinese cardiology and cardiovasology field. Int J Cardiol
          44. Qi Y: Hongfang Shao, Zhiguang Duan: Research groups of oncology co-authorship network in China . Scientometrics 2011, 89:553–67.View Article
          45. Qi Y, Hongfang S, Zhiguang D: The research collaboration in Chinese cardiology and cardiovasology field. Int J Cardiol In Press
          46. Wang Y, Wu Y, Pan Y, Ma Z, Rousseau R: Scientific collaboration in China as refected in co-authorship. Scientometrics 2005,62(2):183–198.View Article
          47. Stokes TD, Hareley JA: Co-authorship, social structure and influence within Specialities. Soc Stud Sci 1989, 19:101–125.View Article
          48. Michael EB, Stephen BJ, Jonathan WK, Kathleen MC, Frank K, Jacqueline AM: Evolution of Coauthorship in Public Health Services and Systems Research. American Journal of Preventive Medicine 2011,41(1):112–117.View Article
          49. Pre-publication history

            1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1472-6947/​13/​52/​prepub

          Copyright

          © Zhang et al.; licensee BioMed Central Ltd. 2013

          This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.